251 research outputs found
Clinical microbiology with multi-view deep probabilistic models
Clinical microbiology is one of the critical topics of this century. Identification
and discrimination of microorganisms is considered a global public health
threat by the main international health organisations, such as World Health
Organisation (WHO) or the European Centre for Disease Prevention and Control
(ECDC). Rapid spread, high morbidity and mortality, as well as the economic
burden associated with their treatment and control are the main causes of their
impact. Discrimination of microorganisms is crucial for clinical applications, for
instance, Clostridium difficile (C. diff ) increases the mortality and morbidity of
healthcare-related infections. Furthermore, in the past two decades, other bacteria,
including Klebsiella pneumoniae (K. pneumonia), have demonstrated a significant
propensity to acquire antibiotic resistance mechanisms. Consequently, the use of
an ineffective antibiotic may result in mortality. Machine Learning (ML) has the
potential to be applied in the clinical microbiology field to automatise current
methodologies and provide more efficient guided personalised treatments.
However, microbiological data are challenging to exploit owing to the presence
of a heterogeneous mix of data types, such as real-valued high-dimensional data,
categorical indicators, multilabel epidemiological data, binary targets, or even
time-series data representations. This problem, which in the field of ML is known
as multi-view or multi-modal representation learning, has been studied in other
application fields such as mental health monitoring or haematology. Multi-view
learning combines different modalities or views representing the same data to extract
richer insights and improve understanding. Each modality or view corresponds
to a distinct encoding mechanism for the data, and this dissertation specifically
addresses the issue of heterogeneity across multiple views.
In the probabilistic ML field, the exploitation of multi-view learning is also
known as Bayesian Factor Analysis (FA). Current solutions face limitations when
handling high-dimensional data and non-linear associations. Recent research
proposes deep probabilistic methods to learn hierarchical representations of the data,
which can capture intricate non-linear relationships between features. However,
some Deep Learning (DL) techniques rely on complicated representations, which
can hinder the interpretation of the outcomes. In addition, some inference methods
used in DL approaches can be computationally burdensome, which can hinder their
practical application in real-world situations. Therefore, there is a demand for
more interpretable, explainable, and computationally efficient techniques for highdimensional
data. By combining multiple views representing the same information, such as genomic, proteomic, and epidemiologic data, multi-modal representation
learning could provide a better understanding of the microbial world. Hence,
in this dissertation, the development of two deep probabilistic models, that can
handle current limitations in state-of-the-art of clinical microbiology, are proposed.
Moreover, both models are also tested in two real scenarios regarding antibiotic
resistance prediction in K. pneumoniae and automatic ribotyping of C. diff in
collaboration with the Instituto de Investigación Sanitaria Gregorio Marañón
(IISGM) and the Instituto Ramón y Cajal de Investigación Sanitaria (IRyCIS).
The first presented algorithm is the Kernelised Sparse Semi-supervised Heterogeneous
Interbattery Bayesian Analysis (SSHIBA). This algorithm uses a kernelised
formulation to handle non-linear data relationships while providing compact representations
through the automatic selection of relevant vectors. Additionally, it
uses an Automatic Relevance Determination (ARD) over the kernel to determine
the input feature relevance functionality. Then, it is tailored and applied to the
microbiological laboratories of the IISGM and IRyCIS to predict antibiotic resistance
in K. pneumoniae. To do so, specific kernels that handle Matrix-Assisted
Laser Desorption Ionization (MALDI)-Time-Of-Flight (TOF) mass spectrometry
of bacteria are used. Moreover, by exploiting the multi-modal learning between
the spectra and epidemiological information, it outperforms other state-of-the-art
algorithms. Presented results demonstrate the importance of heterogeneous models
that can analyse epidemiological information and can automatically be adjusted for
different data distributions. The implementation of this method in microbiological
laboratories could significantly reduce the time required to obtain resistance results
in 24-72 hours and, moreover, improve patient outcomes.
The second algorithm is a hierarchical Variational AutoEncoder (VAE) for
heterogeneous data using an explainable FA latent space, called FA-VAE. The
FA-VAE model is built on the foundation of the successful KSSHIBA approach for
dealing with semi-supervised heterogeneous multi-view problems. This approach
further expands the range of data domains it can handle. With the ability to
work with a wide range of data types, including multilabel, continuous, binary,
categorical, and even image data, the FA-VAE model offers a versatile and powerful
solution for real-world data sets, depending on the VAE architecture. Additionally,
this model is adapted and used in the microbiological laboratory of IISGM, resulting
in an innovative technique for automatic ribotyping of C. diff, using MALDI-TOF
data. To the best of our knowledge, this is the first demonstration of using any
kind of ML for C. diff ribotyping. Experiments have been conducted on strains
of Hospital General Universitario Gregorio Marañón (HGUGM) to evaluate the
viability of the proposed approach. The results have demonstrated high accuracy
rates where KSSHIBA even achieved perfect accuracy in the first data collection.
These models have also been tested in a real-life outbreak scenario at the HGUGM,
where successful classification of all outbreak samples has been achieved by FAVAE. The presented results have not only shown high accuracy in predicting
each strain’s ribotype but also revealed an explainable latent space. Furthermore,
traditional ribotyping methods, which rely on PCR, required 7 days while FA-VAE
has predicted equal results on the same day. This improvement has significantly
reduced the time response by helping in the decision-making of isolating patients
with hyper-virulent ribotypes of C. diff on the same day of infection. The promising
results, obtained in a real outbreak, have provided a solid foundation for further
advancements in the field. This study has been a crucial stepping stone towards
realising the full potential of MALDI-TOF for bacterial ribotyping and advancing
our ability to tackle bacterial outbreaks.
In conclusion, this doctoral thesis has significantly contributed to the field of
Bayesian FA by addressing its drawbacks in handling various data types through
the creation of novel models, namely KSSHIBA and FA-VAE. Additionally, a
comprehensive analysis of the limitations of automating laboratory procedures in
the microbiology field has been carried out. The shown effectiveness of the newly
developed models has been demonstrated through their successful implementation in
critical problems, such as predicting antibiotic resistance and automating ribotyping.
As a result, KSSHIBA and FA-VAE, both in terms of their technical and practical
contributions, signify noteworthy progress both in the clinical and the Bayesian
statistics fields. This dissertation opens up possibilities for future advancements in
automating microbiological laboratories.La microbiología clínica es uno de los temas críticos de este siglo. La identificación
y discriminación de microorganismos se considera una amenaza mundial
para la salud pública por parte de las principales organizaciones internacionales de
salud, como la Organización Mundial de la Salud (OMS) o el Centro Europeo para
la Prevención y Control de Enfermedades (ECDC). La rápida propagación, alta
morbilidad y mortalidad, así como la carga económica asociada con su tratamiento
y control, son las principales causas de su impacto. La discriminación de microorganismos
es crucial para aplicaciones clínicas, como el caso de Clostridium difficile
(C. diff ), el cual aumenta la mortalidad y morbilidad de las infecciones relacionadas
con la atención médica. Además, en las últimas dos décadas, otros tipos de bacterias,
incluyendo Klebsiella pneumoniae (K. pneumonia), han demostrado una
propensión significativa a adquirir mecanismos de resistencia a los antibióticos. En
consecuencia, el uso de un antibiótico ineficaz puede resultar en un aumento de la
mortalidad. El aprendizaje automático (ML) tiene el potencial de ser aplicado en
el campo de la microbiología clínica para automatizar las metodologías actuales y
proporcionar tratamientos personalizados más eficientes y guiados.
Sin embargo, los datos microbiológicos son difíciles de explotar debido a la
presencia de una mezcla heterogénea de tipos de datos, tales como datos reales de
alta dimensionalidad, indicadores categóricos, datos epidemiológicos multietiqueta,
objetivos binarios o incluso series temporales. Este problema, conocido en el campo
del aprendizaje automático (ML) como aprendizaje multimodal o multivista, ha
sido estudiado en otras áreas de aplicación, como en el monitoreo de la salud mental
o la hematología. El aprendizaje multivista combina diferentes modalidades o vistas
que representan los mismos datos para extraer conocimientos más ricos y mejorar la
comprensión. Cada vista corresponde a un mecanismo de codificación distinto para
los datos, y esta tesis aborda particularmente el problema de la heterogeneidad
multivista.
En el campo del aprendizaje automático probabilístico, la explotación del aprendizaje
multivista también se conoce como Análisis de Factores (FA) Bayesianos.
Las soluciones actuales enfrentan limitaciones al manejar datos de alta dimensionalidad
y correlaciones no lineales. Investigaciones recientes proponen métodos
probabilísticos profundos para aprender representaciones jerárquicas de los datos,
que pueden capturar relaciones no lineales intrincadas entre características. Sin
embargo, algunas técnicas de aprendizaje profundo (DL) se basan en representaciones
complejas, dificultando así la interpretación de los resultados. Además, algunos métodos de inferencia utilizados en DL pueden ser computacionalmente
costosos, obstaculizando su aplicación práctica. Por lo tanto, existe una demanda de
técnicas más interpretables, explicables y computacionalmente eficientes para datos
de alta dimensionalidad. Al combinar múltiples vistas que representan la misma
información, como datos genómicos, proteómicos y epidemiológicos, el aprendizaje
multimodal podría proporcionar una mejor comprensión del mundo microbiano.
Dicho lo cual, en esta tesis se proponen el desarrollo de dos modelos probabilísticos
profundos que pueden manejar las limitaciones actuales en el estado del arte de la
microbiología clínica. Además, ambos modelos también se someten a prueba en
dos escenarios reales relacionados con la predicción de resistencia a los antibióticos
en K. pneumoniae y el ribotipado automático de C. diff en colaboración con el
IISGM y el IRyCIS.
El primer algoritmo presentado es Kernelised Sparse Semi-supervised Heterogeneous
Interbattery Bayesian Analysis (SSHIBA). Este algoritmo utiliza una
formulación kernelizada para manejar correlaciones no lineales proporcionando representaciones
compactas a través de la selección automática de vectores relevantes.
Además, utiliza un Automatic Relevance Determination (ARD) sobre el kernel
para determinar la relevancia de las características de entrada. Luego, se adapta
y aplica a los laboratorios microbiológicos del IISGM y IRyCIS para predecir la
resistencia a antibióticos en K. pneumoniae. Para ello, se utilizan kernels específicos
que manejan la espectrometría de masas Matrix-Assisted Laser Desorption
Ionization (MALDI)-Time-Of-Flight (TOF) de bacterias. Además, al aprovechar el
aprendizaje multimodal entre los espectros y la información epidemiológica, supera
a otros algoritmos de última generación. Los resultados presentados demuestran la
importancia de los modelos heterogéneos ya que pueden analizar la información
epidemiológica y ajustarse automáticamente para diferentes distribuciones de datos.
La implementación de este método en laboratorios microbiológicos podría reducir
significativamente el tiempo requerido para obtener resultados de resistencia en
24-72 horas y, además, mejorar los resultados para los pacientes.
El segundo algoritmo es un modelo jerárquico de Variational AutoEncoder
(VAE) para datos heterogéneos que utiliza un espacio latente con un FA explicativo,
llamado FA-VAE. El modelo FA-VAE se construye sobre la base del enfoque de
KSSHIBA para tratar problemas semi-supervisados multivista. Esta propuesta
amplía aún más el rango de dominios que puede manejar incluyendo multietiqueta,
continuos, binarios, categóricos e incluso imágenes. De esta forma, el modelo
FA-VAE ofrece una solución versátil y potente para conjuntos de datos realistas,
dependiendo de la arquitectura del VAE. Además, este modelo es adaptado y
utilizado en el laboratorio microbiológico del IISGM, lo que resulta en una técnica
innovadora para el ribotipado automático de C. diff utilizando datos MALDI-TOF.
Hasta donde sabemos, esta es la primera demostración del uso de cualquier tipo
de ML para el ribotipado de C. diff. Se han realizado experimentos en cepas del Hospital General Universitario Gregorio Marañón (HGUGM) para evaluar la
viabilidad de la técnica propuesta. Los resultados han demostrado altas tasas de
precisión donde KSSHIBA incluso logró una clasificación perfecta en la primera
colección de datos. Estos modelos también se han probado en un brote real
en el HGUGM, donde FA-VAE logró clasificar con éxito todas las muestras del
mismo. Los resultados presentados no solo han demostrado una alta precisión
en la predicción del ribotipo de cada cepa, sino que también han revelado un
espacio latente explicativo. Además, los métodos tradicionales de ribotipado, que
dependen de PCR, requieren 7 días para obtener resultados mientras que FA-VAE
ha predicho resultados correctos el mismo día del brote. Esta mejora ha reducido
significativamente el tiempo de respuesta ayudando así en la toma de decisiones
para aislar a los pacientes con ribotipos hipervirulentos de C. diff el mismo día
de la infección. Los resultados prometedores, obtenidos en un brote real, han
sentado las bases para nuevos avances en el campo. Este estudio ha sido un paso
crucial hacia el despliegue del pleno potencial de MALDI-TOF para el ribotipado
bacteriana avanzado así nuestra capacidad para abordar brotes bacterianos.
En conclusión, esta tesis doctoral ha contribuido significativamente al campo
del FA Bayesiano al abordar sus limitaciones en el manejo de tipos de datos
heterogéneos a través de la creación de modelos noveles, concretamente, KSSHIBA
y FA-VAE. Además, se ha llevado a cabo un análisis exhaustivo de las limitaciones de
la automatización de procedimientos de laboratorio en el campo de la microbiología.
La efectividad de los nuevos modelos, en este campo, se ha demostrado a través de su
implementación exitosa en problemas críticos, como la predicción de resistencia a los
antibióticos y la automatización del ribotipado. Como resultado, KSSHIBA y FAVAE,
tanto en términos de sus contribuciones técnicas como prácticas, representan
un progreso notable tanto en los campos clínicos como en la estadística Bayesiana.
Esta disertación abre posibilidades para futuros avances en la automatización de
laboratorios microbiológicos.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Juan José Murillo Fuentes.- Secretario: Jerónimo Arenas García.- Vocal: María de las Mercedes Marín Arriaz
Multispectral imaging for presymptomatic analysis of light leaf spot in oilseed rape
Background: The use of spectral imaging within the plant phenotyping and breeding community has been increasing due its utility as a non-invasive diagnostic tool. However, there is a lack of imaging systems targeted specifically at plant science duties, resulting in low precision for canopy-scale measurements. This study trials a prototype multispectral system designed specifically for plant studies and looks at its use as an early detection system for visually asymptomatic disease phases, in this case Pyrenopeziza brassicae in Brassica napus. The analysis takes advantage of machine learning in the form of feature selection and novelty detection to facilitate the classification. An initial study into recording the morphology of the samples is also included to allow for further improvement to the system performance. Results: The proposed method was able to detect light leaf spot infection with 92% accuracy when imaging entire oilseed rape plants from above, 12 days after inoculation and 13 days before the appearance of visible symptoms. False colour mapping of spectral vegetation indices was used to quantify disease severity and its distribution within the plant canopy. In addition, the structure of the plant was recorded using photometric stereo, with the output influencing regions used for diagnosis. The shape of the plants was also recorded using photometric stereo, which allowed for reconstruction of the leaf angle and surface texture, although further work is needed to improve the fidelity due to uneven lighting distributions, to allow for reflectance compensation. Conclusions: The ability of active multispectral imaging has been demonstrated along with the improvement in time taken to detect light leaf spot at a high accuracy. The importance of capturing structural information is outlined, with its effect on reflectance and thus classification illustrated. The system could be used in plant breeding to enhance the selection of resistant cultivars, with its early and quantitative capability
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Learning to See with Minimal Human Supervision
Deep learning has significantly advanced computer vision in the past decade, paving the way for practical applications such as facial recognition and autonomous driving. However, current techniques depend heavily on human supervision, limiting their broader deployment. This dissertation tackles this problem by introducing algorithms and theories to minimize human supervision in three key areas: data, annotations, and neural network architectures, in the context of various visual understanding tasks such as object detection, image restoration, and 3D generation.
First, we present self-supervised learning algorithms to handle in-the-wild images and videos that traditionally require time-consuming manual curation and labeling. We demonstrate that when a deep network is trained to be invariant to geometric and photometric transformations, representations from its intermediate layers are highly predictive of object semantic parts such as eyes and noses. This insight offers a simple unsupervised learning framework that significantly improves the efficiency and accuracy of few-shot landmark prediction and matching. We then present a technique for learning single-view 3D object pose estimation models by utilizing in-the-wild videos where objects turn (e.g., cars in roundabouts). This technique achieves competitive performance with respect to existing state-of-the-art without requiring any manual labels during training. We also contribute an Accidental Turntables Dataset, containing a challenging set of 41,212 images of cars in cluttered backgrounds, motion blur, and illumination changes that serve as a benchmark for 3D pose estimation.
Second, we address variations in labeling styles across different annotators, which leads to a type of noisy label referred to as heterogeneous label. This variability in human annotation can cause subpar performance during both the training and testing phases. To mitigate this, we have developed a framework that models the labeling styles of individual annotators, reducing the impact of human annotation variations and enhancing the performance of standard object detection models. We have also applied this framework to analyze ecological data, which are often collected opportunistically across different case studies without consistent annotation guidelines. Through this application, we have obtained several insightful observations into large-scale bird migration behaviors and their relationship to climate change.
Our next study explores the challenges of designing neural networks, an area that lacks a comprehensive theoretical understanding. By linking deep neural networks with Gaussian processes, we propose a novel Bayesian interpretation of the deep image prior, which parameterizes a natural image as the output of a convolutional network with random parameters and random input. This approach offers valuable insights to optimize the design of neural networks for various image restoration tasks.
Lastly, we introduce several machine-learning techniques to reconstruct and edit 3D shapes from 2D images with minimal human effort. We first present a generic multi-modal generative model that bridges 2D images and 3D shapes via a shared latent space, and demonstrate its applications on versatile 3D shape generation and manipulation tasks. Additionally, we develop a framework for joint estimation of 3D neural scene representation and camera poses. This approach outperforms prior works and allows us to operate in the general SE(3) camera pose setting, unlike the baselines. The results also indicate this method can be complementary to classical structure-from-motion (SfM) pipelines as it compares favorably to SfM on low-texture and low-resolution images
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
Inferring Facial and Body Language
Machine analysis of human facial and body language is a challenging topic in computer
vision, impacting on important applications such as human-computer interaction and visual
surveillance. In this thesis, we present research building towards computational frameworks
capable of automatically understanding facial expression and behavioural body language.
The thesis work commences with a thorough examination in issues surrounding facial
representation based on Local Binary Patterns (LBP). Extensive experiments with different
machine learning techniques demonstrate that LBP features are efficient and effective for
person-independent facial expression recognition, even in low-resolution settings. We then
present and evaluate a conditional mutual information based algorithm to efficiently learn the
most discriminative LBP features, and show the best recognition performance is obtained by
using SVM classifiers with the selected LBP features. However, the recognition is performed
on static images without exploiting temporal behaviors of facial expression.
Subsequently we present a method to capture and represent temporal dynamics of facial
expression by discovering the underlying low-dimensional manifold. Locality Preserving Projections
(LPP) is exploited to learn the expression manifold in the LBP based appearance
feature space. By deriving a universal discriminant expression subspace using a supervised
LPP, we can effectively align manifolds of different subjects on a generalised expression manifold.
Different linear subspace methods are comprehensively evaluated in expression subspace
learning. We formulate and evaluate a Bayesian framework for dynamic facial expression
recognition employing the derived manifold representation. However, the manifold representation
only addresses temporal correlations of the whole face image, does not consider
spatial-temporal correlations among different facial regions. We then employ Canonical Correlation Analysis (CCA) to capture correlations among face
parts. To overcome the inherent limitations of classical CCA for image data, we introduce
and formalise a novel Matrix-based CCA (MCCA), which can better measure correlations in
2D image data. We show this technique can provide superior performance in regression and
recognition tasks, whilst requiring significantly fewer canonical factors. All the above work
focuses on facial expressions. However, the face is usually perceived not as an isolated object
but as an integrated part of the whole body, and the visual channel combining facial and
bodily expressions is most informative.
Finally we investigate two understudied problems in body language analysis, gait-based
gender discrimination and affective body gesture recognition. To effectively combine face
and body cues, CCA is adopted to establish the relationship between the two modalities, and
derive a semantic joint feature space for the feature-level fusion. Experiments on large data
sets demonstrate that our multimodal systems achieve the superior performance in gender
discrimination and affective state analysis.Research studentship of Queen Mary, the International Travel Grant of the Royal Academy of Engineering,
and the Royal Society International Joint Project
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